Mineração de regras de associação temporais quantitativas por meio de algoritmo genético
Resumo
Mineração de regras de associação tem mostrado grande potencial para extrair conhecimento de conjunto de dados multidimensionais. Contudo, os métodos existentes na literatura não são efetivamente aplicáveis a dados temporais quantitativos. Este artigo estende os conceitos de mineração de regras de associação da literatura. Com base nestes conceitos é apresentado um método para mineração de regras de conjuntos de dados multidimensionais temporais quantitativos por meio de algoritmo genético, denominado GTARGA em referência à Quantitative Temporal Association Rule Mining by Genetic Algorithm. Experimentos com QTARGA em várias bases de dados reais mostram que este permite minerar várias regras de alta confiança em uma única execução do método.
Palavras-chave:
Algoritmos genéticos, mineração de regras de associação, dados temporais quantitativos
Referências
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C.-H. Lee, M.-S. Chen, and C.-R. Lin. Progressive partition miner: an efficient algorithm for mining general temporal association rules. IEEE Transaction on Knowledge and Data Engineering, 15(4):1004–1017, 2003.
D. Martin, A. Rosete, J. Alcala-Fdez, and F. Herrera. A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. Evolutionary Computation, IEEE Transactions on, 18(1):54–69, 2014.
D.-A. Chiang, C.-T. Wang, S.-P. Chen, and C.-C. Chen. The cyclic model analysis on sequential patterns. IEEE Transactions on Knowledge and Data Engineering, 21(11):1617–1628, 2009.
E. Georgii, L. Richter, U. Rückert, and S. Kramer. Analyzing microarray data using quantitative association rules. Bioinformatics, 21(suppl 2):ii123–ii129, 2005.
H. Zheng, J. He, G. Huang, and Y. Zhang. Optimized fuzzy association rule mining for quantitative data. In 2014 IEEE International Conference on Fuzzy Systems, pages 396–403, Beijing, China, 2014.
J. K. Febrer-Hern´andez and J. Hern´andez-Palancar. Sequential pattern mining algorithms review. Intelligent Data Analysis, 16:451–466, 2012.
M. Mart´ınez-Ballesteros, A. Troncoso, F. Mart´ınez-Alvarez, and J. C. Riquelme. Mining ´ quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering, 17(3):227–242, 2010.
M. Plantevit, A. Laurent, D. Laurent, M. Teisseire, and Y. W. Choong. Mining multidimensional and multilevel sequential patterns. ACM Transactions on Knowledge Discovery from Data, 4(1):1–37, 2010.
M. Zhang and C. He. Survey on association rules mining algorithms. Advancing Computing, Communication, Control and Management, 56:111–118, 2010.
N. R. Mabroukeh and C. I. Ezeife. A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(1):1–41, 2010.
N. Tatti and B. Cule. Mining closed strict episodes. Data Mining and Knowledge Discovery, 25(1):34–66, 2011.
S. Amo, N. A. Silva, R. P. Silva, and F. S. Pereira. Tree pattern mining with tree automata constraints. Information Systems, 35:570–591, 2010.
T. Fu. A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164–181, 2011.
T.-P. Hong, C.-S. Kuo, and S.-C. Chi. Mining association rules from quantitative data. Intelligent Data Analysis, 3(5):363–376, 1999.
X. Yan, C. Zhang, and S. Zhang. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications, 36:3066–3076, 2009.
Y. Hai and X. Li. A general temporal association rule frequent itemsets mining algorithm. International Journal of Advancements in Computing Technology, 3(11):63–71, 2011.
Y. J. Lee, J. W. Lee, D. J. Chai, B. H. Hwang, and K. H. Ryu. Mining temporal interval relational rules from temporal data. Journal of Systems and Software, 82(1):155–167, 2009.
B. Catania and A. Maddalena. A unified framework for heterogeneous patterns. Information Systems, 37:460–483, 2012.
B. Pei, S. Zhao, H. Chen, X. Zhou, and D. Chen. Farp: Mining fuzzy association rules from a probabilistic quantitative database. Information Sciences, 237:242 – 260, 2013.
C.-H. Lee, M.-S. Chen, and C.-R. Lin. Progressive partition miner: an efficient algorithm for mining general temporal association rules. IEEE Transaction on Knowledge and Data Engineering, 15(4):1004–1017, 2003.
D. Martin, A. Rosete, J. Alcala-Fdez, and F. Herrera. A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. Evolutionary Computation, IEEE Transactions on, 18(1):54–69, 2014.
D.-A. Chiang, C.-T. Wang, S.-P. Chen, and C.-C. Chen. The cyclic model analysis on sequential patterns. IEEE Transactions on Knowledge and Data Engineering, 21(11):1617–1628, 2009.
E. Georgii, L. Richter, U. Rückert, and S. Kramer. Analyzing microarray data using quantitative association rules. Bioinformatics, 21(suppl 2):ii123–ii129, 2005.
H. Zheng, J. He, G. Huang, and Y. Zhang. Optimized fuzzy association rule mining for quantitative data. In 2014 IEEE International Conference on Fuzzy Systems, pages 396–403, Beijing, China, 2014.
J. K. Febrer-Hern´andez and J. Hern´andez-Palancar. Sequential pattern mining algorithms review. Intelligent Data Analysis, 16:451–466, 2012.
M. Mart´ınez-Ballesteros, A. Troncoso, F. Mart´ınez-Alvarez, and J. C. Riquelme. Mining ´ quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering, 17(3):227–242, 2010.
M. Plantevit, A. Laurent, D. Laurent, M. Teisseire, and Y. W. Choong. Mining multidimensional and multilevel sequential patterns. ACM Transactions on Knowledge Discovery from Data, 4(1):1–37, 2010.
M. Zhang and C. He. Survey on association rules mining algorithms. Advancing Computing, Communication, Control and Management, 56:111–118, 2010.
N. R. Mabroukeh and C. I. Ezeife. A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(1):1–41, 2010.
N. Tatti and B. Cule. Mining closed strict episodes. Data Mining and Knowledge Discovery, 25(1):34–66, 2011.
S. Amo, N. A. Silva, R. P. Silva, and F. S. Pereira. Tree pattern mining with tree automata constraints. Information Systems, 35:570–591, 2010.
T. Fu. A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164–181, 2011.
T.-P. Hong, C.-S. Kuo, and S.-C. Chi. Mining association rules from quantitative data. Intelligent Data Analysis, 3(5):363–376, 1999.
X. Yan, C. Zhang, and S. Zhang. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications, 36:3066–3076, 2009.
Y. Hai and X. Li. A general temporal association rule frequent itemsets mining algorithm. International Journal of Advancements in Computing Technology, 3(11):63–71, 2011.
Y. J. Lee, J. W. Lee, D. J. Chai, B. H. Hwang, and K. H. Ryu. Mining temporal interval relational rules from temporal data. Journal of Systems and Software, 82(1):155–167, 2009.
Publicado
26/05/2015
Como Citar
SILVA, Sérgio; BATISTA, Marcos; TRAINA, Agma.
Mineração de regras de associação temporais quantitativas por meio de algoritmo genético. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 11. , 2015, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 195-202.
DOI: https://doi.org/10.5753/sbsi.2015.5817.
